Extracting Cardiac and Respiratory Self-Gating Signals from Magnetic Resonance Imaging Data
Sammanfattning: Motion artefacts due to cardiac and respiratory motion present a daily challenge in cardiac Magnetic Resonance Imaging (MRI), and many different motion correction procedures are used in clinical routine imaging. To reduce motion artefacts further, patients are required to hold their breath during parts of the data acquisition, which is physically straining – especially when done repetitively. Self-Gating (SG) is a method that extracts cardiac and respiratory motion information from the MRI data in the form of signals, called SG signals, and uses them to divide the data into the specific cardiac and respiratory phases it was acquired from. This method both avoids motion artefacts and allow for free-breathing acquisition. This project’s goal was to find a method for extracting cardiac and respiratory SG signals from MRI data. The data was acquired with a golden angle radial acquisition method for 3-dimensional (3D) scans. Extraction of the raw signal was tested for both raw k-space data and high temporal resolution image series, where the images were reconstructed using a sliding window reconstruction. Filters were then applied to isolate the cardiac and respiratory information, to create separate cardiac and respiratory SG signals. Thereafter trigger points marking the beginning of the cardiac and respiratory cycles were generated. The trigger points were compared against ECG and respiratory trigger points provided by the MR scanner. The conclusion was that the SG signals based on k-space data was functional on the scans from the evaluated subjects and the most effective choice of the two options, but image based SG signals may prove to be functional after further studies.
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